Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns

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Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns (2005)

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by Yejin Choi , Claire Cardie

Venue:	In HLT/EMNLP 2005
Citations:	101 - 21 self

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@INPROCEEDINGS{Choi05identifyingsources,
    author = {Yejin Choi and Claire Cardie},
    title = {Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns},
    booktitle = {In HLT/EMNLP 2005},
    year = {2005}
}

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Abstract

Recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source identification as a sequence tagging task, AutoSlog learns extraction patterns. Our results show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79.3 % precision and 59.5 % recall using a head noun matching measure, and 81.2 % precision and 60.6% recall using an overlap measure. 1

Keyphrases

conditional random field extraction pattern opinion analysis hybrid approach source identification method performs information extraction task recent system system identifies opinion source sentiment classification opinion recognition head noun matching measure overlap measure

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